Replies: 2 comments
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As for The core ideas for understanding the
During forecast
For example, we have T timestamps in history, N segments and H horizon. The resulting table for learning catboost will have T * N rows (this is lower if we remove rows with missing values). The resulting table for predicting will have H * N rows. We use a regular CatBoostRegressor, but for each point in the forecasting horizon there is a separate row in a table with its own features. To determine which approach is better per-segment or multi-segment you should use backtest and look at the metrics.
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As for hierarchical time series reconciliation, you are using a very old version of documentation. I advise to look at the documentation for the version you are currently using. Documentation for the latest available version is here. I think that good enough explanation about current implemented reconciliation methods is in the tutorial about hierarchical time series. |
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📖 Documentation improvement
pls how to find in documentation
1
https://docs.etna.ai/1.15.1/reconciliation.html
what algorithms are used for hierarchical time series reconciliation
2
how CatBoostMultiSegmentModel is better than CatBoostPerSegmentModel
and what algorithm used for CatBoostMultiSegmentModel
or at least where in code CatBoostMultiSegmentModel algorithm is coded
Additional context
No response
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